Vectors, quaternions, and matrices for general purposes, and computer graphics.

Vector, matrix, and quaternion data structures and operations. Uses f32 or f64 based types. SoA SIMD support.

Fundamental types:

• Vec3
• Vec4
• Quaternion
• Mat3
• Mat4
• Vec3x8,
• Vec3x16,
• Quaternionx8,
• (etc)

Example use cases:

• Computer graphics
• Biomechanics
• Robotics and unmanned aerial vehicles
• Structural chemistry and biochemistry
• Cosmology modeling
• Various scientific and engineering applications
• Aircraft attitude systems and autopilots

Vector and Quaternion types are copy.

For Compatibility with no_std targets, e.g. embedded, disable default features, and enable the no_std feature. This omits SIMD, std::fmt::Display implementations, and enables num_traits's libm capabilities for certain operations. lin_alg = { version = "^1.1.3", default-features = false, features = ["no_std"] }

For computer-graphics functionality (e.g. specialty matrix constructors, and [de]serialization to byte arrays for passing to and from GPUs), use the computer_graphics feature. For bincode binary encoding and decoding, use the encode feature.

For information on practical quaternion operations: Quaternions: A practical guide.

The From trait is implemented for most types, for converting between f32 and f64 variants using the into() syntax.

SIMD

Includes SIMD constructs with structure of array (SoA) layout, for Vec and Quaternion types. For example: Vec3x8, f64::Vec3x4, Vec4x8, and Quaternionx8. These allow you to perform parallel operations on vectors and quaternions (e.g. 4, 8, or 16 at a time) for a similar computation cost as a single one. They are currently configured with 256-bit wide (AVX) values, performing operations on 8 f32 types, or 4 f64 types. See the examples below for details.

This library exposes an f32x8 SIMD type that wraps __m256 with appropriate constructors, operator overloads etc, and similar. It's used internally by the SIMD Vec and Quaternion types. This, and the Vec3x8, Quaternionx8 etc APIs, mimic the nightly core::simd library. They're used internally by our SIMD vector and quaternion types. It also includes f64x4. We are waiting to add 512-bit wide types until their operations are in stable rust. Hopefully soon!

We use these custom SIMD types vice core::simd so this library will work on stable rust. We'll remove these when core::simd is stable.

This lib also includes pack and unpack utility functions for converting slices of f32, Vec3, Quaternion etc between SIMD and normal (scalar) values. This handles padding the last chunk, since input data may not align evenly in chunks of 4, 8, or 16. It also includes a generic pack_slice for packing Copy type items into arrays, generally for use with SIMD types.

Note: This approach is the opposite of the array of structures (AoS) approach to SIMD used by the glam and cgmath libraries.

CUDA (GPU)

This library includes two helper functions for use with the cudarc library; these are to allocated Vec3 and Quaternion types. (f32 and f64). They perform host-to-device copies. This is intended to simply application code.

pub fn alloc_vec3s(dev: &Arc<CudaDevice>, data: &[Vec3]) -> CudaSlice<f32> {}

pub fn alloc_quaternions(dev: &Arc<CudaDevice>, data: &[Quaternion]) -> CudaSlice<f32> {}

A note on performance

For performance-sensitive operations, depending on the details of your computation and hardware, you may wish to use a mix of GPU (CUDA, or graphics shaders), parallelization via threads (e.g. Rayon), and SIMD operations. This library aims to assist in these operations, and leaves details to the application.

Examples

See the official documentation (Linked above) for details. Below is a brief, impractical syntax overview:

use core::f32::consts::TAU;

use lin_alg::f32::{Vec3, Quaternion};

fn main() {
    let _ = Vec3::new_zero();
    
    let a = Vec3::new(1., 1., 1.);
    let b = Vec3::new(0., -1., 10.);
    
    let mut c = a + b;
    
    let d = a.dot(b);
    
    c.normalize(); // or:
    let e = c.to_normalized();
    
    let f = a.cross(b);
    
    let g = Quaternion::from_unit_vecs(d, e);
    
    let h = g.inverse();
    
    let k = Quaternion::new_identity();
    
    let l = k.rotate_vec(c);
    
    l.magnitude();
    
    let m = Quaternion::from_axis_angle(Vec3::new(1., 0., 0.), TAU / 16.);
}

If using for computer graphics, this functionality may be helpful:

    let a = Vec3::new(1., 1., 1.);
    let bytes = a.to_bytes(); // Send this to the GPU. `Quaternion` and `MatN` have similar methods.

    let model_mat = Mat4::new_translation(self.position)
        * self.orientation.to_matrix()
        * Mat4::new_scaler_partial(self.scale);

    let proj_mat = Mat4::new_perspective_lh(self.fov_y, self.aspect, self.near, self.far);

    let view_mat = self.orientation.inverse().to_matrix() * Mat4::new_translation(-self.position);

    // Example of rolling a camera around the forward axis:
    let fwd = orientation.rotate_vec(FWD_VEC);
    let rotation = Quaternion::from_axis_angle(fwd, -rotate_key_amt);
    orientation = rotation * orientation;

A practical geometry example:

/// Calculate the dihedral angle between 4 positions (3 bonds).
/// The `bonds` are one atom's position, substracted from the next. Order matters.
pub fn calc_dihedral_angle(bond_middle: Vec3, bond_adjacent1: Vec3, bond_adjacent2: Vec3) -> f64 {
    // Project the next and previous bonds onto the plane that has this bond as its normal.
    // Re-normalize after projecting.
    let bond1_on_plane = bond_adjacent1.project_to_plane(bond_middle).to_normalized();
    let bond2_on_plane = bond_adjacent2.project_to_plane(bond_middle).to_normalized();

    // Not sure why we need to offset by 𝜏/2 here, but it seems to be the case
    let result = bond1_on_plane.dot(bond2_on_plane).acos() + TAU / 2.;

    // The dot product approach to angles between vectors only covers half of possible
    // rotations; use a determinant of the 3 vectors as matrix columns to determine if what we
    // need to modify is on the second half.
    let det = det_from_cols(bond1_on_plane, bond2_on_plane, bond_middle);

    if det < 0. { result } else { TAU - result }
}

A SIMD example of vector operations:

use lin_alg::f32:{Vec3, Vec3x8};

// Non-SIMD Vec3s we'll start with.
let vec_a = Vec3::new(1., 2., 3.);
let vec_b = Vec3::new(4., 5., 6.);

// An example where we copy the same Vec3 into all 8 slots. In most practical uses,
// each slot will contain a different value.
let a = Vec3x8::from_array([vec_a; 8]);
let b = Vec3x8::from_array([vec_b; 8]);

// Perform vector addition on 8 Vec3s at once.
let c = a + b;

// Create a [Vec3; 8].
let d = a.cross(b).to_array();

// Create a `f32x8`, then convert to an array.
let dot_result = a.dot(b).to_array();

let e = vec_a * 3.;
let f = vec_a * f32x8::from_array([3.; 8]);
let g = vec_a * f32x8::splat(3.)

A SIMD example of rotating vectors.

use core::f32::consts::TAU;
use lin_alg::f32::{Quaternion, Vec3, Quaternionx8, Vec3x8};

let rot_init = [
    Quaternion::from_unit_vecs(UP, FORWARD),
    Quaternion::from_unit_vecs(UP, -FORWARD),
    Quaternion::from_unit_vecs(UP, RIGHT),
    Quaternion::from_unit_vecs(UP, -RIGHT),
    Quaternion::from_unit_vecs(UP, UP),
    Quaternion::from_unit_vecs(UP, -UP),
    Quaternion::from_axis_angle(RIGHT, TAU/4.),
    Quaternion::from_axis_angle(RIGHT, TAU/8.),
];

let rotation = Quaternionx8::from_array(rot_init);

// This could be 8 separate values.
let vec = Vec3x8::from_array([UP; 8]);

let result = rotation.rotate_vec(vec).to_array();

let sqrt_2_div_2 = 2_f32.sqrt()/2.;
let angled = Vec3::new(0., -sqrt_2_div_2, sqrt_2_div_2);

assert!((result[0] - FORWARD).magnitude() < f32::EPSILON);
assert!((result[1] - -FORWARD).magnitude() < f32::EPSILON);
assert!((result[2] - RIGHT).magnitude() < f32::EPSILON);
assert!((result[3] - -RIGHT).magnitude() < f32::EPSILON);
assert!((result[4] - UP).magnitude() < f32::EPSILON);
assert!((result[5] - -UP).magnitude() < f32::EPSILON);
assert!((result[6] - -FORWARD).magnitude() < f32::EPSILON);
assert!((result[7] - angled).magnitude() < f32::EPSILON);

An example function using SIMD for a practical use, integrating Vec3x8s with SIMD types directly.

use lin_alg::f32::{Vec3, Vec3x8, f32x8, unpack_vec3};

// ...

fn run_lj(atom_0_posits: &[Vec3], atom_1_posits: &[Vec3]) {
    // Convert all Vec3s to their SIMD variants, and loop through them. This converts then to 
    // `Vec<Vec3x8>`
    let (atom_0_posits_x8, valid_lanes_last_0) = pack_vec3(&atom_0_posits);
    let (atom_1_posits_x8, valid_lanes_last_0) = pack_vec3(&atom_1_posits);
    
    // todo: Or, parellilize with Rayon.
    for i in 0..atom_0_posits_simd {
        let atom_1 = atom_0_posits_x8[i];       
        let atom_0 = atom_1_posits_x8[i];       
        
        lj_potential(atom_0_posit, atom_1_posit, // ...);)
    }
// ...
    
    // In practice here, you may wish to sum components, being careful to discard (or render harmless) values
    // from the pad lanes in the last chunk. If collecting the values directly instead. (prior to processing),
    // you can use these `unpack` methods, available for `f32`, `f64`, `Vec3`, and `Quaternion`. They automatically
    // remove the padding lanes.
    let atom_0_posits_processed = unpack_vec3(atom_0_posits_simd, atom_0_posits.len());
}


fn lj_potential(
    atom_0_posit: Vec3x8,
    atom_1_posit: Vec3x8,
    atom_0_els: [Element; 8],
    atom_1_els: [Element; 8],
) -> f32x8 {
    // This line demonstrates use of this library; the rest of the code below
    // is for context. We have already partitioned a set of `Vec3` into 
    // `Vec3x8`, grouped in blocks of 8, prior to this function.
    let r = (atom_0_posit - atom_1_posit).magnitude(); // This is a Vec3x8.

    let mut sig = [0.0; 8];
    let mut eps = [0.0; 8];
    for i in 0..8 {
        (sig[i], eps[i]) = get_lj_params(atom_0_els[i], atom_1_els[i], lj_lut)
    }

    let sig_ = f32x8::from_slice(&sig);
    let eps_ = f32x8::from_array(eps);

    let sr = sig_ / r;
    let sr6 = sr.powi(6);
    let sr12 = sr6.powi(2);
    
    f32x8::splat(4.) * eps_ * (sr12 - sr6)
}

This example shows a few different SIMD operations. It mixes Vec3s, floating point types, and arrays of non-numerical variables, synchronized.

fn bodies_from_atomsx8(atoms: &[Atom]) -> Vec<BodyVdwx8> {
    let mut posits: Vec<Vec3> = Vec::with_capacity(atoms.len());
    let mut els = Vec::with_capacity(atoms.len());
    
    for atom in atoms {
        posits.push(atom.posit.into());
        els.push(atom.element);
    }

    let (posits_x8, _valid_lanes) = pack_vec3(&posits);

    let (els_x8, _) = pack_slice::<_, 8>(&els);
    let mut result = Vec::with_capacity(posits_x8.len());

    for (i, posit) in posits_x8.iter().enumerate() {
        let masses: Vec<_> = els_x8[i].iter().map(|el| el.atomic_number() as f32).collect();
        let mass = f32x8::from_slice(&masses);

        result.push(BodyVdwx8 {
            posit: *posit,
            vel: Vec3x8::new_zero(),
            accel: Vec3x8::new_zero(),
            mass,
            element: els_x8[i],
        })
    }

    result
}